Commodity Consumption by Population Characteristics

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Food Consumption

ERS tracks the supply of food available for consumption in the
United States and examines consumer food preferences by age,
income, region, race, place where food is eaten, and other
characteristics. Descriptive statistics of commodity consumption
help to inform growers about who consumes their commodities, how
and where their commodities are used, and how much is consumed.
This information facilitates improved promotion strategies by the
food and agriculture industry. In addition, USDA agencies have used
commodity consumption data for regulatory analyses.

Commodity Consumption Studies: 1994-1998

USDA has conducted periodic surveys of household and individual
food intakes in the United States since the 1930s. In these
surveys, respondents provided a list of foods consumed as well as
information on where and how much of each food was eaten.
Various economic, social, and demographic characteristics were also
collected for the respondent and his/her household.

ERS researchers have used several technical databases
from USDA's Agricultural Research Service (ARS) to analyze
commodity consumption using data collected under the 1994-1998
Continuing Survey of Food Intakes by Individuals (CSFII). These
databases include recipes, the Pyramid Servings Database, and the
Food Commodity Intake Database. A series of analyses
describing commodity consumption by age, income, region, race, and
eating location was completed between 2001 and 2007 (see Eating Patterns:
Who Eats What, Where, and How Much). Building upon these
descriptive studies, ERS researchers conducted regression analyses
to predict future food and commodity consumption (see
Food and Agricultural Commodity Consumption in the United States:
Looking Ahead to 2020).

Food and Commodity Consumption Studies: 1999-2002

The U.S. Department of Health and Human Services (HHS) has
conducted food consumption studies similar to those conducted by
USDA. Since 2002, USDA and HHS have jointly conducted an integrated
food consumption survey for the Nation, titled What We Eat in
America (WWEIA), as part of the National Health and Nutrition
Examination Survey (NHANES).

With the release of the FICRCD data, commodity
consumption by age and gender has been tabulated for the three
surveys--1994-1998, 1999-2000, and 2001-2002. The 1999-2000 and
2001-2002 NHANES data were combined to allow reporting of commodity
consumption by household income, age and gender, body weight
status, race and ethnicity, and education attainment as well as by
the location where foods were eaten. The results (FICRCD results)
are used to disaggregate ERS's Food
Availability (Per Capita) Data System by the same
delineations. The disaggregated food availability data are
reported in 7 tables and the FICRCD results (means and standard
errors) are reported in 7 tables. The sample sizes for NHANES
1999-2002 by respondents' characteristics are summarized in a
table.

Some major findings are highlighted below.

U.S. consumption of fruits and vegetables rises with income.
The differences between low-income households (less than 185
percent of the poverty line) and high-income households (greater
than 300 percent of the poverty line) are particularly
significant.

Children consume more apples than adults, mainly in the form of
apple juice. Seventy percent of apple consumption among children
age 2-19 comes from apple juice, compared with 62 percent for
adults.

Milk consumption correlates with educational attainment, with
the consumption of skim milk being highly associated with
education. Adults who attended college consumed 19 percent more
milk than those who did not finish high school. The consumption of
skim milk among college-educated adults was more than double the
amount consumed by those who dropped out of high school.

Black Americans consume the largest amounts of pork, chicken,
turkey, and fish, whereas Hispanics consume the largest amount of
eggs.

Healthy-weight adults consume more vegetables than overweight
and obese adults, but the differences are not statistically
significant.